the training data set below. Your goal is to build a classifier to predict whether a patient

should or should not be prescribed contact lenses. The classifier will output $1$ when it

predicts that contact lenses should be prescribed and $0$ when it predicts that they should

not be prescribed. The output class is in the last column "Contact Lenses" and the input

attributes are: "Patient Age", "Astigmatic", "Tear Production", and "Spectacle." More

specifically, you will compare Na$$ve Bayes $($NB$)$ and Decision Tree $($DT$).$

For Na$$ve Bayes $($NB$),$ you will use $m-$estimate from the lecture with $m=2$ and $p=0\mathrm{.}5$

for probability estimations.

For Decision Tree $($DT$),$ you will follow the lecture's code to build your trees with$-$

out pruning except that multiple$-$way splitting is allowed and use Information Gain $($IG$)$

to select the best attribute. In the case of ties, break ties in favor of the leftmost feature.

$($a$)(18$ points$)$ Compare the performance of NB vs$.$ DT using $2-$fold cross$-$validation

$($CV$)$ and report their $2-$fold CV accuracy. For the $i$ th fold, the testing

dataset is composed of all the data points whose $($ID mod$2=i-1).$

For each fold, show the induced Na$$ve Bayes $($in order of left to right

columns$)$ and DT models.

$($b$)(6$ points$)$ Based on the $2-$fold CV accuracy from $($a$),$ which classifier, NB or

DT$,$ would you choose? Report your final model for the selected classifier.